12 citations · 14 across the 9 of their papers we have counts for
9 papers
Steering Vision-Language-Action Models as Anti-Exploration: A Test-Time Scaling Approach
Siyuan Yang, Yang Zhang, Haoran He +4
Vision-Language-Action (VLA) models, trained via flow-matching or diffusion objectives, excel at learning complex behaviors from large-scale, multi-modal datasets (e.g., human tele…
Task-Agnostic Pre-training and Task-Guided Fine-tuning for Versatile Diffusion Planner
Chenyou Fan, Chenjia Bai, Zhao Shan +3
Diffusion models have demonstrated their capabilities in modeling trajectories of multi-tasks. However, existing multi-task planners or policies typically rely on task-specific dem…
SAM-E: Leveraging Visual Foundation Model with Sequence Imitation for Embodied Manipulation
Junjie Zhang, Chenjia Bai, Haoran He +5
Acquiring a multi-task imitation policy in 3D manipulation poses challenges in terms of scene understanding and action prediction. Current methods employ both 3D representation and…
Regularized Conditional Diffusion Model for Multi-Task Preference Alignment
Xudong Yu, Chenjia Bai, Haoran He +2
Sequential decision-making is desired to align with human intents and exhibit versatility across various tasks. Previous methods formulate it as a conditional generation process, u…
Learning an Actionable Discrete Diffusion Policy via Large-Scale Actionless Video Pre-Training
Haoran He, Chenjia Bai, Ling Pan +3
Learning a generalist embodied agent capable of completing multiple tasks poses challenges, primarily stemming from the scarcity of action-labeled robotic datasets. In contrast, a…
Diffusion Models for Reinforcement Learning: A Survey
Zhengbang Zhu, Hanye Zhao, Haoran He +5
Diffusion models surpass previous generative models in sample quality and training stability. Recent works have shown the advantages of diffusion models in improving reinforcement…
Robust Quadrupedal Locomotion via Risk-Averse Policy Learning
Jiyuan Shi, Chenjia Bai, Haoran He +6
The robustness of legged locomotion is crucial for quadrupedal robots in challenging terrains. Recently, Reinforcement Learning (RL) has shown promising results in legged locomotio…
On the Value of Myopic Behavior in Policy Reuse
Kang Xu, Chenjia Bai, Shuang Qiu +5
Leveraging learned strategies in unfamiliar scenarios is fundamental to human intelligence. In reinforcement learning, rationally reusing the policies acquired from other tasks or…
Bridging the Sim-to-Real Gap from the Information Bottleneck Perspective
Haoran He, Peilin Wu, Chenjia Bai +5
Reinforcement Learning (RL) has recently achieved remarkable success in robotic control. However, most works in RL operate in simulated environments where privileged knowledge (e.g…